Suppress callback references

main.py

@@ -4,19 +4,18 @@
 
 import os
 from pathlib import Path
-from concurrent.futures import ThreadPoolExecutor
 
 import polars as pl
 import dash
 from dash import dcc, html, Input, Output
 import dash_bootstrap_components as dbc
-import numpy as np
 
 from parser import parse_log, parse_csv
 from decoder import decode_j1939_frames
 from stats.utils.extractor import load_data
-from stats.id_viewer import _format_can_id_vec, plot_bits
+
 from stats.frequency import calculate_frequency, plot_frequency
+from stats.id_viewer import prepare_data, plot_bits
 from stats.correlation import calculate_correlation, plot_correlation_heatmap
 from stats.entropy import calculate_byte_entropy, plot_entropy_heatmap
 
@@ -30,15 +29,15 @@ BUS2_DECODED = "data/parquet/bus2_decoded.parquet"
 
 def run_pipeline():
     os.makedirs("data/logs", exist_ok=True)
-    os.makedirs("data/csv", exist_ok=True)
-    os.makedirs("data/parquet", exist_ok=True)
     if not Path(BUS1_DECODED).exists() or not Path(BUS2_DECODED).exists():
         print("Parsing raw log...")
         parse_log(RAW_LOG, BUS1_CSV, BUS2_CSV)
 
         print("Converting to parquet...")
-        parse_csv(BUS1_CSV).sink_parquet(BUS1_PARQUET)
+        lf1 = parse_csv(BUS1_CSV)
-        parse_csv(BUS2_CSV).sink_parquet(BUS2_PARQUET)
+        lf1.sink_parquet(BUS1_PARQUET)
+        lf2 = parse_csv(BUS2_CSV)
+        lf2.sink_parquet(BUS2_PARQUET)
 
         print("Decoding J1939...")
         df1 = pl.read_parquet(BUS1_PARQUET)
@@ -56,70 +55,27 @@ DATA = {
     "Bus 2": load_data(BUS2_DECODED)
 }
 
-PRECOMPUTED_FIGURES = {}
-DATA_BY_ID = {}
-CORR_CACHE = {}
-
-def process_bus_data(bus, df):
-    precomp = {}
-    precomp[f"{bus}_freq"] = plot_frequency(calculate_frequency(df), title=f"{bus} Frequency")
-    precomp[f"{bus}_entropy"] = plot_entropy_heatmap(calculate_byte_entropy(df), title=f"{bus} Byte-Level Entropy")
-
-    can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
-    formatted = _format_can_id_vec(df[can_id_col])
-    df = df.assign(Formatted_ID=formatted)
-
-    df = df.sort_values(['Formatted_ID', 'Timestamp'], kind='stable')
-
-    grouped = {}
-    for can_id, group in df.groupby(by='Formatted_ID'):
-        byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in group.columns]
-        if not group.empty and len(byte_cols) > 0:
-            arr = group[byte_cols].to_numpy(dtype=np.float32, copy=False)
-            if len(arr) > 1:
-                changed = np.any(arr[1:] != arr[:-1], axis=1)
-                keep = np.concatenate(([True], changed))
-                group = group.iloc[keep]
-        grouped[can_id] = (group, byte_cols)
-
-    return precomp, grouped
-
-with ThreadPoolExecutor() as executor:
-    futures = {executor.submit(process_bus_data, bus, df): bus for bus, df in DATA.items()}
-    for future in futures:
-        bus = futures[future]
-        precomp, grouped = future.result()
-        PRECOMPUTED_FIGURES.update(precomp)
-        DATA_BY_ID[bus] = grouped
-
 app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
 app.config.suppress_callback_exceptions = True
 
 app.layout = dbc.Container([
-    html.H1("CANveyor", className="my-4"),
+    html.H1("CAN Bus Analyzer", className="my-4"),
+    dbc.Row([
+        dbc.Col(html.Label("Select Bus:"), width=1, className="mt-2"),
+        dbc.Col(dcc.Dropdown(
+            id='bus-selector',
+            options=[{'label': k, 'value': k} for k in DATA.keys()],
+            value='Bus 1',
+            clearable=False
+        ), width=2),
+    ], className="mb-3"),
     dbc.Tabs([
-        dbc.Tab(label="Overview", tab_id="overview", children=[
+        dbc.Tab(label="Frequency", tab_id="freq"),
-            html.Div(id="overview-content")
+        dbc.Tab(label="ID Viewer", tab_id="id_viewer"),
-        ]),
+        dbc.Tab(label="Correlation", tab_id="corr"),
-        dbc.Tab(label="Statistics", tab_id="statistics", children=[
+        dbc.Tab(label="Entropy", tab_id="entropy"),
-            dbc.Row([
+    ], id="tabs", active_tab="freq"),
-                dbc.Col(html.Label("Select Bus:"), width=1, className="mt-2"),
+    html.Div(id="tab-content", className="mt-3")
-                dbc.Col(dcc.Dropdown(
-                    id='bus-selector',
-                    options=[{'label': k, 'value': k} for k in DATA.keys()],
-                    value='Bus 1',
-                    clearable=False
-                ), width=2),
-            ], className="mb-3 mt-3"),
-            dbc.Tabs([
-                dbc.Tab(label="Frequency", tab_id="freq"),
-                dbc.Tab(label="ID Viewer", tab_id="id_viewer"),
-                dbc.Tab(label="Correlation", tab_id="corr"),
-                dbc.Tab(label="Entropy", tab_id="entropy"),
-            ], id="tabs", active_tab="freq"),
-            html.Div(id="tab-content", className="mt-3")
-        ])
-    ], id="main-tabs", active_tab="statistics")
 ], fluid=True)
 
 @app.callback(
@@ -131,10 +87,12 @@ def render_content(tab, bus):
     df = DATA[bus]
 
     if tab == 'freq':
-        return dcc.Graph(figure=PRECOMPUTED_FIGURES[f"{bus}_freq"], style={'height': '80vh'})
+        stats = calculate_frequency(df)
+        fig = plot_frequency(stats, title=f"{bus} Frequency")
+        return dcc.Graph(figure=fig, style={'height': '80vh'})
 
     elif tab == 'id_viewer':
-        ids = sorted(DATA_BY_ID[bus].keys())
+        ids = sorted(df['ID'].unique().tolist())
         return html.Div([
             html.Label("Select CAN ID:"),
             dcc.Dropdown(
@@ -148,7 +106,7 @@ def render_content(tab, bus):
         ])
 
     elif tab == 'corr':
-        ids = sorted(DATA_BY_ID[bus].keys())
+        ids = sorted(df['ID'].unique().tolist())
         return html.Div([
             dbc.Row([
                 dbc.Col(html.Label("Method:"), width=1, className="mt-2"),
@@ -170,7 +128,9 @@ def render_content(tab, bus):
         ])
 
     elif tab == 'entropy':
-        return dcc.Graph(figure=PRECOMPUTED_FIGURES[f"{bus}_entropy"], style={'height': '80vh'})
+        entropy_df = calculate_byte_entropy(df)
+        fig = plot_entropy_heatmap(entropy_df, title=f"{bus} Byte-Level Entropy")
+        return dcc.Graph(figure=fig, style={'height': '80vh'})
 
     return html.Div("Tab not found")
 
@@ -183,12 +143,8 @@ def render_content(tab, bus):
 def update_id_viewer(selected_id, bus, tab):
     if tab != 'id_viewer' or not selected_id:
         return dash.no_update
-
+    df = DATA[bus]
-    grouped_data = DATA_BY_ID.get(bus, {})
+    filtered_df, byte_cols = prepare_data(df, selected_id)
-    if selected_id not in grouped_data:
-        return dash.no_update
-
-    filtered_df, byte_cols = grouped_data[selected_id]
     return plot_bits(filtered_df, byte_cols, selected_id, title=f"{bus} Byte Visualization")
 
 @app.callback(
@@ -201,21 +157,12 @@ def update_id_viewer(selected_id, bus, tab):
 def update_corr(method, target, bus, tab):
     if tab != 'corr':
         return dash.no_update
-
+    df = DATA[bus]
     target_id = None if target == 'all' or not target else target
-    cache_key = (bus, method, target_id)
+    corr_df = calculate_correlation(df, method=method, target_id=target_id)
-
-    if cache_key not in CORR_CACHE:
-        df = DATA[bus]
-        corr_df = calculate_correlation(df, method=method, target_id=target_id)
-        CORR_CACHE[cache_key] = corr_df
-    else:
-        corr_df = CORR_CACHE[cache_key]
-
     title = f"{bus} Correlation"
     if target_id:
         title += f" ({target_id})"
-
     return plot_correlation_heatmap(corr_df, target_id=target_id, title=title)
 
 if __name__ == '__main__':

pyproject.toml

@@ -1,15 +1,7 @@
 [project]
 name = "CANveyor"
-version = "0.1.0"
+version = "0.0.4"
 description = "J1939 CAN bus parser that works in pair with CANdigger"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.14"
-dependencies = [
+dependencies = ["polars", "pathlib", "typing"]
-    "polars",
-    "dash",
-    "dash-bootstrap-components",
-    "numpy",
-    "pandas",
-    "plotly",
-    "plotly-resampler"
-]

stats/correlation.py

@@ -4,7 +4,6 @@
 
 import argparse
 from pathlib import Path
-from concurrent.futures import ThreadPoolExecutor
 
 import numpy as np
 import pandas as pd
@@ -28,7 +27,8 @@ def _ensure_int_bytes(df: pd.DataFrame, cols: list) -> pd.DataFrame:
     return df
 
 def calculate_correlation(df: pd.DataFrame, method: str, target_id: str | None = None) -> pd.DataFrame:
-    available_cols = [f"b{i}" for i in range(8) if f"b{i}" in df.columns]
+    byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in df.columns]
+    available_cols = [col for col in byte_cols if col in df.columns]
 
     if not available_cols:
         raise ValueError("No byte columns (b0-b7) found in the DataFrame")
@@ -70,7 +70,8 @@ def calculate_correlation(df: pd.DataFrame, method: str, target_id: str | None =
     else:
         groups = []
 
-    def _process_group(sub):
+    out = np.zeros((len(unique_ids), n_cols), dtype=np.float64)
+    for gi, sub in enumerate(groups):
         mask = ~np.isnan(sub).any(axis=1)
         sub = sub[mask]
         if len(sub) > 1:
@@ -80,11 +81,7 @@ def calculate_correlation(df: pd.DataFrame, method: str, target_id: str | None =
                 c = np.abs(np.corrcoef(sub, rowvar=False))
             np.nan_to_num(c, copy=False, nan=0.0)
             np.fill_diagonal(c, 0.0)
-            return c.max(axis=0)
+            out[gi] = c.max(axis=0)
-        return np.zeros(n_cols, dtype=np.float64)
-
-    with ThreadPoolExecutor() as executor:
-        out = np.array(list(executor.map(_process_group, groups)))
 
     result = pd.DataFrame(out, index=unique_ids, columns=available_cols)
     result.index.name = 'Identifier'

stats/entropy.py

@@ -4,7 +4,6 @@
 
 import argparse
 from pathlib import Path
-from concurrent.futures import ThreadPoolExecutor
 
 import numpy as np
 import pandas as pd
@@ -37,7 +36,8 @@ def _entropy_col(a: np.ndarray) -> float:
     return float(-np.sum(p * np.log2(p)))
 
 def calculate_byte_entropy(df: pd.DataFrame) -> pd.DataFrame:
-    available_cols = [f"b{i}" for i in range(8) if f"b{i}" in df.columns]
+    byte_cols = [f"b{i}" for i in range(8) if f"b{i}" in df.columns]
+    available_cols = byte_cols
     if not available_cols:
         raise ValueError("No byte columns (b0-b7) found in the DataFrame")
 
@@ -64,14 +64,10 @@ def calculate_byte_entropy(df: pd.DataFrame) -> pd.DataFrame:
     else:
         groups = []
 
-    def _process_group(sub):
+    out = np.zeros((len(unique_ids), n_cols), dtype=np.float64)
-        res = np.zeros(n_cols, dtype=np.float64)
+    for gi, sub in enumerate(groups):
         for ci in range(n_cols):
-            res[ci] = _entropy_col(sub[:, ci])
+            out[gi, ci] = _entropy_col(sub[:, ci])
-        return res
-
-    with ThreadPoolExecutor() as executor:
-        out = np.array(list(executor.map(_process_group, groups)))
 
     result = pd.DataFrame(out, index=unique_ids, columns=available_cols)
     result.index.name = 'Identifier'

stats/frequency.py

@@ -18,6 +18,7 @@ def _format_can_id_vec(s: pd.Series) -> pd.Series:
 def calculate_frequency(df: pd.DataFrame) -> pd.DataFrame:
     can_id_col = 'ID' if 'ID' in df.columns else 'Identifier'
     formatted = _format_can_id_vec(df[can_id_col])
+    df['Formatted_ID'] = formatted
 
     counts = formatted.value_counts()
     freq_df = pd.DataFrame({

stats/id_viewer.py

@@ -7,7 +7,7 @@ from pathlib import Path
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
-from plotly_resampler import FigureResampler
+import lttbc
 from stats.utils.extractor import load_data
 
 def _format_can_id_vec(s: pd.Series) -> pd.Series:
@@ -41,24 +41,29 @@ def prepare_data(df, target_id):
     return filtered, byte_cols
 
 def plot_bits(df, byte_cols, can_id, title):
-    fig = FigureResampler(
+    fig = go.Figure()
-        resampled_trace_prefix_suffix=("", ""),
-        show_mean_aggregation_size=False
-    )
     colors = ['#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33', '#a65628', '#f781bf']
     n = len(byte_cols)
+    max_points = 2000
 
     x = df['Timestamp'].to_numpy() if not df.empty else np.array([])
     for i, col in enumerate(byte_cols):
         y = df[col].to_numpy(dtype=np.float32, copy=False) if not df.empty else np.array([])
 
-        fig.add_trace(go.Scatter(
+        if len(x) > max_points and len(x) == len(y):
+            x_plot, y_plot = lttbc.downsample(x, y, max_points)
+        else:
+            x_plot, y_plot = x, y
+
+        fig.add_trace(go.Scattergl(
+            x=x_plot,
+            y=y_plot,
             mode='lines',
             line=dict(shape='hv', width=2, color=colors[i % len(colors)]),
             name=col.upper(),
             legendgroup=col.upper(),
             hovertemplate=f"<b>{col.upper()}</b><br>Time: %{{x}}<br>Value: %{{y}}<extra></extra>",
-        ), hf_x=x, hf_y=y)
+        ))
 
     all_button = dict(label='ALL', method='restyle', args=[{'visible': [True] * n}])
     none_button = dict(label='NONE', method='restyle', args=[{'visible': ['legendonly'] * n}])